{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Google Trends API.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPytzS+wvzo79baWvAKzrvc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Tanu-N-Prabhu/Python/blob/master/Google_Trends_API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4pXe__UmetC",
        "colab_type": "text"
      },
      "source": [
        "# Google Trends API for Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzFAuTuHmfiU",
        "colab_type": "text"
      },
      "source": [
        "## In this tutorial, I will demonstrate how to use the Google Trends API for getting the current trending topics on the internet.\n",
        "\n",
        "\n",
        "![alt text](https://cdn-images-1.medium.com/max/1200/1*Fi6masemXJT3Q8YWekQCDQ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ptTzrFtmlig",
        "colab_type": "text"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "[Google trends](https://trends.google.com/trends/?geo=US) is a website that analyzes and lists the popular search results on Google search based on various regions and languages. Google Trends is Google's website (obviously). With the help of this tutorial, you can get the trending results and many more from google trends website using python. You don't need to manually search and copy the trending results, the Python API called `pytrends` does the job for you. Before getting started, I want all of you guys to go through the official documentation of the `pytrends` API."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZ03EX8JmqNK",
        "colab_type": "text"
      },
      "source": [
        "[pytrends API](https://pypi.org/project/pytrends/)\n",
        "\n",
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gM9A4G54mtLD",
        "colab_type": "text"
      },
      "source": [
        "# Installation\n",
        "\n",
        "The first step is to install the library manually. So, open your favorite IDE or notebook start typing the following code. I will use [Google Colab](https://colab.research.google.com/) because it's my favorite notebook.\n",
        "\n",
        "\n",
        "\n",
        "> If you are using jupyter notebook, just type the code as it is (make sure you have '!' at the beginning)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ID0raHgkmvps",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "3a27caaf-6557-4981-f51a-c8c35c0180da"
      },
      "source": [
        "!pip install pytrends"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pytrends\n",
            "  Downloading https://files.pythonhosted.org/packages/74/a4/c1b1242be7d31650c6d9128a776c753db18f0e83290aaea0dd80dd31374b/pytrends-4.7.2.tar.gz\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from pytrends) (2.21.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pytrends) (0.25.3)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.6/dist-packages (from pytrends) (4.2.6)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pytrends) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pytrends) (2019.11.28)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pytrends) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pytrends) (3.0.4)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas->pytrends) (1.17.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pytrends) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->pytrends) (2.6.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->pytrends) (1.12.0)\n",
            "Building wheels for collected packages: pytrends\n",
            "  Building wheel for pytrends (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pytrends: filename=pytrends-4.7.2-cp36-none-any.whl size=14261 sha256=5a1f8aa2c4faceb04879594e0001d7609754ac0a814150f49363199ed7b8bc8a\n",
            "  Stored in directory: /root/.cache/pip/wheels/64/ae/af/51d48fbbca0563036c6f80999b7ce3f097fa591fd165047baf\n",
            "Successfully built pytrends\n",
            "Installing collected packages: pytrends\n",
            "Successfully installed pytrends-4.7.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrLYHTJUmzw4",
        "colab_type": "text"
      },
      "source": [
        "Or, if you are using an IDE, just type the following code\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUOPJhkJm2yW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install pytrends"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "711Pz41Im6YU",
        "colab_type": "text"
      },
      "source": [
        "After executing the above code you should get a successful message as shown above\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXPG2708m9QW",
        "colab_type": "text"
      },
      "source": [
        "# Implementation\n",
        "\n",
        "## Connecting to Google\n",
        "\n",
        "You must connect to Google first because after all, we are requesting the Google trending topics from Google Trends. For this, we need to import the method called `TrendReq` from `pytrends.request` library. Also, I will import the pandas library to store and visualize the data which you see in the later tutorial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ujx4eIkm_Mt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd                        \n",
        "from pytrends.request import TrendReq\n",
        "pytrend = TrendReq()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_SpSbpAnAd0",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrWLhzqhnBBr",
        "colab_type": "text"
      },
      "source": [
        "## Interest By Region\n",
        "\n",
        "Let us see the terms which are popular in the region worldwide. I will choose, the term to be searched as \"Taylor Swift\" (I like her so….)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4lmPUmqnEYJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "b3eb99c3-3c8d-4f04-a17c-35f408daf1ff"
      },
      "source": [
        "pytrend.build_payload(kw_list=['Taylor Swift'])\n",
        "# Interest by Region\n",
        "df = pytrend.interest_by_region()\n",
        "df.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Taylor Swift</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>geoName</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>American Samoa</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Anguilla</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antarctica</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antigua &amp; Barbuda</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Taylor Swift\n",
              "geoName                        \n",
              "Afghanistan                   0\n",
              "Albania                       0\n",
              "Algeria                      16\n",
              "American Samoa                0\n",
              "Andorra                       0\n",
              "Angola                        0\n",
              "Anguilla                      0\n",
              "Antarctica                    0\n",
              "Antigua & Barbuda             0\n",
              "Argentina                    19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "im_lE1KCnG9z",
        "colab_type": "text"
      },
      "source": [
        "**Now you might be thinking what are the values, what do they denote?** \n",
        "\n",
        "The values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location which is half as popular. A value of 0 indicates a location where there was not enough data for this term. Source → [Google Trends](https://support.google.com/trends/answer/4355212).."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6ORJ6I1nIrl",
        "colab_type": "text"
      },
      "source": [
        "Let us plot the result on a bar graph because sometimes visual representation gives a clear picture."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8FHZeUenKJf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "outputId": "855b9eb3-b6d7-442e-9f78-7f8e97698405"
      },
      "source": [
        "df.reset_index().plot(x='geoName', y='Taylor Swift', figsize=(120, 10), kind ='bar')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff024a40b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
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5mKpYe6W3QeV7WLl3XNP2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L0+gKTfAyvanhS3lyZtHULV7EiYxxoH7FOodSjDpLbKWF1FjWvRVYrMvylpBPA3SV8DnqZH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IP3eWnDZtynMEk32AfgT8y9hImq/tv02/vnZaOibJh4Et2l46LpkkrT5tr07yNGAmGii1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\nyQ7ATwZnkhbDZm2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            "text/plain": [
              "<Figure size 8640x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSupqQa5nMrm",
        "colab_type": "text"
      },
      "source": [
        "Also, you use the parameter `resolution = 'COUNTRY_NAME'` to filter the results.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjH2DvbEnPcm",
        "colab_type": "text"
      },
      "source": [
        "## Daily Search Trends\n",
        "\n",
        "Now let us get the top daily search trends worldwide. To do this we have to use the `trending_searches()` method. If you want to search worldwide just don't pass any parameter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vqkob1NnQqK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "62b5137d-81aa-4b05-db74-8b846ad4cc86"
      },
      "source": [
        "# Get Google Hot Trends data\n",
        "df = pytrend.trending_searches(pn='united_states')\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>South Carolina primary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>David Byrne</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Liverpool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lakers vs Grizzlies</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tom Steyer</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        0\n",
              "0  South Carolina primary\n",
              "1             David Byrne\n",
              "2               Liverpool\n",
              "3     Lakers vs Grizzlies\n",
              "4              Tom Steyer"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0J4Fjt98nSf4",
        "colab_type": "text"
      },
      "source": [
        "Make sure you enter the country name in lowercase `pn = \"canada\"` . Also, you can compare the above results with the [google trend's result](https://trends.google.com/trends/trendingsearches/daily?geo=US). To get today's trending topics just use:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoCNGlSknVrO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pytrend.today_searches(pn='US')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fkse9VmunYXs",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rt3XMTXtnXug",
        "colab_type": "text"
      },
      "source": [
        "## Top Charts\n",
        "\n",
        "Let was see what was trending in 2019. With the help of `top_charts` method we can get the top trending searches yearly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opEutdKdnaLO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "588557f5-3707-4a87-db1f-ee69358355a2"
      },
      "source": [
        "# Get Google Top Charts\n",
        "df = pytrend.top_charts(2019, hl='en-US', tz=300, geo='GLOBAL')\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>exploreQuery</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India vs South Africa</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cameron Boyce</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Copa America</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bangladesh vs India</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>iPhone 11</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   title exploreQuery\n",
              "0  India vs South Africa             \n",
              "1          Cameron Boyce             \n",
              "2           Copa America             \n",
              "3    Bangladesh vs India             \n",
              "4              iPhone 11             "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOBAPjkCnbvP",
        "colab_type": "text"
      },
      "source": [
        "To compare the results just visit [Google Trends](https://trends.google.com/trends/yis/2019/GLOBAL/). We can specify the year and the country that we want to see the trending searches.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v44c23GqndXK",
        "colab_type": "text"
      },
      "source": [
        "## Google Keyword Suggestions\n",
        "\n",
        "Let us see how can we obtain google's keyword suggestion. If you don't know what I'm talking about. The below image explains things more clear.\n",
        "\n",
        "![alt text](https://cdn-images-1.medium.com/max/1200/1*QRpWWBS1SHXBr71Jp1NwQA.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i90jZxfBnjVW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "dd86178b-a782-4f07-aa29-440dbe50d986"
      },
      "source": [
        "# Get Google Keyword Suggestions\n",
        "keywords = pytrend.suggestions(keyword='Mercedes Benz')\n",
        "df = pd.DataFrame(keywords)\n",
        "df.drop(columns= 'mid')   # This column makes no sense"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mercedes-Benz</td>\n",
              "      <td>Automobile company</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mercedes-Benz A-Class</td>\n",
              "      <td>Car model</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mercedes-Benz</td>\n",
              "      <td>Automobile make</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mercedes-Benz E-Class</td>\n",
              "      <td>Car model</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mercedes-Benz GLB-Class</td>\n",
              "      <td>Car model</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     title                type\n",
              "0            Mercedes-Benz  Automobile company\n",
              "1    Mercedes-Benz A-Class           Car model\n",
              "2            Mercedes-Benz     Automobile make\n",
              "3    Mercedes-Benz E-Class           Car model\n",
              "4  Mercedes-Benz GLB-Class           Car model"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hzn4OMgn2Ag",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZcJVdWyn1Ph",
        "colab_type": "text"
      },
      "source": [
        "## Related Queries\n",
        "\n",
        "It's a common thing that when a user searches for a topic, they would also search for something related. These are called related queries. Let us see what are the related queries for the topic \"***Coronavirus***\". Always remember when you want to change the topic name just run the following code again with the new name as the parameter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLTMY4B9n3v6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pytrend.build_payload(kw_list=['Coronavirus'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9IhWfs_oAqY",
        "colab_type": "text"
      },
      "source": [
        "Now let's run the method `related_queries` which returns a dictionary full of related queries for the topic ***Coronavirus*** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WR-NgLr_oB8_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "outputId": "cb13fa3a-b1cf-4c8f-a500-e53ded0181d2"
      },
      "source": [
        "# Related Queries, returns a dictionary of dataframes\n",
        "related_queries = pytrend.related_queries()\n",
        "related_queries.values()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([{'top':                       query  value\n",
              "0                     virus    100\n",
              "1         virus coronavirus     95\n",
              "2                    corona     92\n",
              "3         china coronavirus     87\n",
              "4                     china     86\n",
              "5      coronavirus symptoms     83\n",
              "6          news coronavirus     72\n",
              "7              corona virus     61\n",
              "8        coronavirus update     53\n",
              "9        coronavirus italia     50\n",
              "10           el coronavirus     37\n",
              "11          coronavirus map     34\n",
              "12        wuhan coronavirus     33\n",
              "13                    wuhan     33\n",
              "14        coronavirus death     31\n",
              "15      what is coronavirus     31\n",
              "16        coronavirus cases     30\n",
              "17          coronavirus usa     30\n",
              "18     sintomas coronavirus     30\n",
              "19           uk coronavirus     23\n",
              "20           us coronavirus     23\n",
              "21  symptoms of coronavirus     22\n",
              "22       coronavirus latest     20\n",
              "23         coronavirus live     20\n",
              "24     coronavirus in china     20, 'rising':                          query   value\n",
              "0            wuhan coronavirus  168350\n",
              "1                        wuhan  165100\n",
              "2          notizie coronavirus   71950\n",
              "3           ultime coronavirus   64800\n",
              "4   coronavirus ultime notizie   57900\n",
              "5           milano coronavirus   43800\n",
              "6        coronavirus lombardia   43450\n",
              "7        coronavirus in italia   37300\n",
              "8      wuhan china coronavirus   36700\n",
              "9          italien coronavirus   28950\n",
              "10            coronavirus roma   28600\n",
              "11          coronavirus veneto   27750\n",
              "12                 wuhan virus   26550\n",
              "13        coronavirus map live   26100\n",
              "14   coronavirus symptoms 2020   24900\n",
              "15           mappa coronavirus   24850\n",
              "16            coronavirus meme   23950\n",
              "17           coronavirus count   23900\n",
              "18      coronavirus death rate   22950\n",
              "19   aggiornamenti coronavirus   22950\n",
              "20         coronavirus muertos   22550\n",
              "21                 kobe bryant   22100\n",
              "22       latest on coronavirus   21000\n",
              "23          coronavirus napoli   20750\n",
              "24          coronavirus torino   18750}])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVFA3LbJoELu",
        "colab_type": "text"
      },
      "source": [
        "Similarly, you can also search for the related topics just run the below code to do so:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrH1dBDJoF2F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "outputId": "b3ff0641-273f-4977-a3bd-bf7773e15753"
      },
      "source": [
        "# Related Topics, returns a dictionary of dataframes\n",
        "related_topic = pytrend.related_topics()\n",
        "related_topic.values()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([{'rising':      value  ...            topic_type\n",
              "0   175600  ...         City in China\n",
              "1    47900  ...        Italian region\n",
              "2    27350  ...           Cooperative\n",
              "3    26650  ...        Italian region\n",
              "4    18550  ...                 Topic\n",
              "5    17800  ...        Italian region\n",
              "6    17600  ...               Website\n",
              "7    15150  ...                 Topic\n",
              "8    13550  ...        Italian region\n",
              "9    12450  ...                Animal\n",
              "10   11000  ...                 Topic\n",
              "11   10500  ...                 Topic\n",
              "12    9950  ...      Water navigation\n",
              "13    9400  ...                 Topic\n",
              "14    2800  ...       Spoken language\n",
              "15    2400  ...          Ethnic group\n",
              "16    2250  ...  Country in East Asia\n",
              "17    2100  ...     Country in Europe\n",
              "18    1050  ...                 Topic\n",
              "19     500  ...                 Topic\n",
              "\n",
              "[20 rows x 6 columns], 'top':     value  ...            topic_type\n",
              "0     100  ...                 Virus\n",
              "1       8  ...                 Topic\n",
              "2       5  ...  Country in East Asia\n",
              "3       5  ...      Infectious agent\n",
              "4       4  ...     Country in Europe\n",
              "5       4  ...                 Topic\n",
              "6       3  ...       Spoken language\n",
              "7       3  ...                 Virus\n",
              "8       3  ...          Ethnic group\n",
              "9       2  ...         City in China\n",
              "10      1  ...               Disease\n",
              "11      1  ...               Disease\n",
              "12      1  ...                 Topic\n",
              "13      1  ...                 Topic\n",
              "14      0  ...        Italian region\n",
              "15      0  ...           Cooperative\n",
              "16      0  ...        Italian region\n",
              "17      0  ...                 Topic\n",
              "18      0  ...        Italian region\n",
              "19      0  ...               Website\n",
              "20      0  ...                 Topic\n",
              "21      0  ...        Italian region\n",
              "22      0  ...                Animal\n",
              "23      0  ...                 Topic\n",
              "24      0  ...                 Topic\n",
              "\n",
              "[25 rows x 7 columns]}])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbI2mxGPoINF",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwloflHUoItB",
        "colab_type": "text"
      },
      "source": [
        "This is the end of the tutorial, I hope you guys have learned a thing or two. If you guys have any doubts regarding the tutorial let me know via the comment section. Although this is a short tutorial there is a lot to learn. Alright see you in my next tutorial, have a good day!!!"
      ]
    }
  ]
}